As a result, practical implementations resort to various heuristics to speed up the algorithm. Popular approaches include GP approximations with a fixed number of basis functions
 [1]}, [2]},
reduction of optimization variables by fixing the state covariances in the optimization problem based on their predicted value at the last MPC iteration [3]}, [4]}, or to completely ignore the uncertainty description provided by the GP posterior covariance in the control algorithm [5]}, [6]}.
